Intelligent Control: Fundamentals and Applications

By Fouad Sabry

What Is Intelligent Control

The term "intelligent control" refers to a category of control methods that make use of a number of different artificial intelligence computing methodologies, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Intelligent Control

Chapter 2: Artificial Intelligence

Chapter 3: Machine Learning

Chapter 4: Reinforcement Learning

Chapter 5: Neural Network

Chapter 6: Adaptive Control

Chapter 7: Computational Intelligence

Chapter 8: Outline of Artificial Intelligence

Chapter 9: Machine Learning Control

Chapter 10: Data-driven Model

(II) Answering the public top questions about intelligent control.

(III) Real world examples for the usage of intelligent control in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent control' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent control.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 3, 2023

Book preview

Chapter 1: Intelligent control

The term intelligent control refers to a category of control methods that make use of a number of different artificial intelligence computing approaches, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms.

The following is a list of the most important subfields that fall under the umbrella of intelligent control::

Neural network control

Machine learning control

Reinforcement learning

Bayesian control

Fuzzy control

Neuro-fuzzy control

Expert Systems

Genetic control

Continuously new control strategies are being developed in tandem with the production of new models of intelligent behavior and the development of computing tools to support these models.

In practically every area of research and technology, the use of neural networks has proved successful in the problem-solving process. The control of neural networks consists mostly of two stages:

System identification

Control

A feedforward network with nonlinear, continuous, and differentiable activation functions has been proven to have the capacity of universal approximation. The identification of systems has also been accomplished via the use of recurrent networks. Input-output data pairs are provided, and the goal of the system identification process is to provide a mapping between these data pairs. It is expected that this kind of network would be able to capture the dynamics of a system. Regarding the control aspect, deep reinforcement learning has shown its capacity to exert authority over intricate systems.

The theory of Bayesian probability has led to the development of a number of algorithms that are now used in a large number of technologically sophisticated control systems. These algorithms serve as state space estimators of various variables that are utilized by the controller.

Bayesian control components include many different types of filters, such the Kalman filter and the particle filter, to name just two examples. To derive the so-called system model and measurement model, which are the mathematical connections relating the state variables to the sensor measurements that are accessible in the controlled system, the Bayesian method of controller design often necessitates a significant amount of work. In this regard, it has a very tight connection to the system-theoretic method of control design.

{End Chapter 1}

Chapter 2: Artificial intelligence

As contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) refers to the intelligence demonstrated by robots. Research in artificial intelligence (AI) has been described as the area of study of intelligent agents, which refers to any system that senses its surroundings and performs actions that optimize its possibility of attaining its objectives. In other words, AI research is a discipline that studies intelligent agents. The term AI impact refers to the process by which activities that were formerly thought to need intelligence but are no longer included in the concept of artificial intelligence as technology advances. AI researchers have adapted and incorporated a broad variety of approaches for addressing issues, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics, in order to tackle these difficulties. Computer science, psychology, linguistics, philosophy, and a great many other academic disciplines all contribute to the development of AI.

The theory that human intellect can be so accurately characterized that a computer may be constructed to imitate it was the guiding principle behind the establishment of this discipline. This sparked philosophical debates concerning the mind and the ethical implications of imbuing artificial organisms with intellect comparable to that of humans; these are topics that have been investigated by myth, literature, and philosophy ever since antiquity.

In ancient times, artificial creatures with artificial intelligence were used in various narrative devices.

and are often seen in works of literature, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.

The formal design for Turing-complete artificial neurons that McCullouch and Pitts developed in 1943 was the first piece of work that is now widely understood to be an example of artificial intelligence.

Attendees of the conference went on to become pioneers in the field of AI research.

They, together with their pupils, were able to build programs that the press referred to as astonishing. These programs included machines that were able to learn checkers techniques, solving word problems in algebra, demonstrating logical theorems and having good command of the English language.

Around the middle of the decade of the 1960s, research done in the United States

was receiving a significant amount of funding from the Department of Defense, and facilities were being set up all around the globe.

as well as continuous pressure from the Congress of the United States to invest in more fruitful endeavors, The United States of America

both the Canadian and British governments stopped funding exploratory research in artificial intelligence.

The following few years would be referred to in the future as a AI winter.

a time when it was difficult to acquire financing for artificial intelligence initiatives.

a kind of artificial intelligence software that mimicked the knowledge and analytical prowess of human professionals.

By 1985, Over a billion dollars was now being transacted in the artificial intelligence business.

While this is going on, The United States and the United Kingdom have reestablished support for university research as a direct result of Japan's computer programme for the fifth generation.

However, When the market for lisp machines crashed in 1987, it was the beginning of a downward spiral.

AI once again fallen into disfavor, as well as another, longer-lasting winter started.

Geoffrey Hinton is credited for reviving interest in neural networks and the concept of connectionism.

Around the middle of the 1980s, David Rumelhart and a few others were involved. During the 1980s, many soft computing tools were created.

include things like neural networks, fuzzy systems, Theory of the grey system, the use of evolutionary computing as well as a number of methods derived from statistical or mathematical optimization.

Through the late 1990s and into the early 21st century, AI worked to progressively rehabilitate its image by developing solutions that were tailored to address particular challenges. Because of the tight emphasis, researchers were able to develop conclusions that could be verified, use a greater number of mathematical approaches, and work with experts from other areas (such as statistics, economics and mathematics). In the 1990s, the solutions that were produced by AI researchers were never referred to as artificial intelligence, but by the year 2000, they were being employed extensively all around the world. According to Jack Clark of Bloomberg, the year 2015 was a watershed year for artificial intelligence. This is due to the fact that the number of software projects that employ AI inside Google went from sporadic use in 2012 to more than 2,700 projects in 2015.

The overarching challenge of emulating (or fabricating) intelligence has been segmented into a variety of more specific challenges. These are certain characteristics or skills that researchers anticipate an intelligent system to possess. The greatest emphasis has been paid to the characteristics that are detailed below.

Researchers in the early days of computer science devised algorithms that mirrored the step-by-step reasoning that people use when they solve problems or make logical inferences. Research in artificial intelligence had by the late 1980s and early 1990s established strategies for coping with uncertain or partial information. These approaches used notions from probability and economics. Even among humans, the kind of step-by-step deduction that early studies in artificial intelligence could replicate is uncommon. They are able to address the majority of their issues by making snap decisions based on their intuition.

Information engineering and the representation of that knowledge are what enable artificial intelligence systems to intelligently respond to inquiries and draw conclusions about real-world events.

An ontology is a collection of objects, relations, ideas, and attributes that are formally characterized in order to ensure that software agents are able to comprehend them. An ontology is a description of what exists. Upper ontologies are ontologies that seek to provide a basis for all other information and operate as mediators between domain ontologies, which cover specialized knowledge about a particular knowledge domain. Upper ontologies are the most broad ontologies, and they are also termed ontologies (field of interest or area of concern). A software that is genuinely intelligent would also require access to commonsense knowledge, which is the collection of facts that the typical human is aware of. In most cases, the description logic of an ontology, such as the Web Ontology Language, is used to express the semantics of an ontology. In addition to other domains, situations, events, states, and times; causes and effects; knowledge about knowledge (what we know about what other people